Research on AIGC-Integrated Design Education for Sustainable Teaching: An Empirical Analysis Based on the TAM and TPACK Models
Ziyang Huang, Xuan Fu, J. Zhao
Abstract
With the rapid proliferation of Generative Artificial Intelligence (AIGC) technologies in higher education, identifying effective integration pathways into design curricula has become a pressing issue in the field of educational technology. This study employs a mixed-methods approach grounded in the Technology Acceptance Model (TAM) and the Technological Pedagogical Content Knowledge (TPACK) framework, incorporating course analysis, questionnaire surveys, structural path modeling, and interview analysis. Focusing on both instructors and students, this research systematically investigates the acceptance, integration mechanisms, and sustainable development potential of AIGC in university-level design education. The findings indicate that students generally acknowledge the value of AIGC in enhancing creativity and improving efficiency, although gaps persist in their understanding of ethical considerations and original expression. On the teachers’ side, technological knowledge exerts a significant positive influence on the integration of content knowledge, while the impact of pedagogical knowledge remains underutilized. Interview data further reveal a structural tension within current teaching practices, characterized by the rapid adoption of technological tools contrasted with the slower evolution of pedagogical systems. Based on these insights, this study offers five strategic recommendations for sustainable teaching, including the development of teacher training systems, curriculum module design, student media literacy enhancement, and pedagogical reconstruction aligned with the Sustainable Development Goals (SDGs). These findings provide both theoretical and practical contributions to the effective and sustainable integration of AIGC into higher design education.